TR-2015001: A Survey and Critique of Facial Expression Synthesis in Sign Language Animation

Sign language animations can lead to better accessibility of information and services for people who are deaf and have low literacy skills in spoken/written languages. Due to the distinct word-order, syntax, and lexicon of the sign language from the spoken/written language, many deaf people find it difficult to comprehend the text on a computer screen or captions on a television. Animated characters performing sign language in a comprehensible way could make this information accessible. Facial expressions and other non-manual components play an important role in the naturalness and understandability of these animations. Their coordination to the manual signs is crucial for the interpretation of the signed message. Software to advance the support of facial expressions in generation of sign language animation could make this technology more acceptable for deaf people. In this survey, we discuss the challenges in facial expression synthesis and we compare and critique the state of the art projects on generating facial expressions in sign language animations. Beginning with an overview of facial expressions linguistics, sign language animation technologies, and some background on animating facial expressions, a discussion of the search strategy and criteria used to select the five projects that are the primary focus of this survey follows. This survey continues on to introduce the work from the five projects under consideration. Their contributions are compared in terms of support for specific sign language, categories of facial expressions investigated, focus range in the animation generation, use of annotated corpora, input data or hypothesis for their approach, and other factors. Strengths and drawbacks of individual projects are identified in the perspectives above. This survey concludes with our current research focus in this area and future prospects.

Face Detection and Facial Feature Localization for multi-pose faces and complex backgroundimages

The objective of this thesis work, is to propose an algorithm to detect the faces in a digital image with complex background. A lot of work has already been done in the area of face detection, but drawback of some face detection algorithms is the lack of ability to detect faces with closed eyes and open mouth. Thus facial features form an important basis for detection. The current thesis work focuses on detection of faces based on facial objects. The procedure is composed of three different phases: segmentation phase, filtering phase and localization phase. In segmentation phase, the algorithm utilizes color segmentation to isolate human skin color based on its chrominance properties. In filtering phase, Minkowski addition based object removal (Morphological operations) has been used to remove the non-skin regions. In the last phase, Image Processing and Computer Vision methods have been used to find the existence of facial components in the skin regions.This method is effective on detecting a face region with closed eyes, open mouth and a half profile face. The experiment’s results demonstrated that the detection accuracy is around 85.4% and the detection speed is faster when compared to neural network method and other techniques.

The facial affective scale as a predictor for pain unpleasantness when children undergo immunizations.

Needle fear is a common problem in children undergoing immunization. To ensure that the individual child's needs are met during a painful procedure it would be beneficial to be able to predict whether there is a need for extra support. The self-reporting instrument facial affective scale (FAS) could have potential for this purpose. The aim of this study was to evaluate whether the FAS can predict pain unpleasantness in girls undergoing immunization. Girls, aged 11-12 years, reported their expected pain unpleasantness on the FAS at least two weeks before and then experienced pain unpleasantness immediately before each vaccination. The experienced pain unpleasantness during the vaccination was also reported immediately after each immunization. The level of anxiety was similarly assessed during each vaccination and supplemented with stress measures in relation to the procedure in order to assess and evaluate concurrent validity. The results show that the FAS is valid to predict pain unpleasantness in 11-12-year-old girls who undergo immunizations and that it has the potential to be a feasible instrument to identify children who are in need of extra support to cope with immunization. In conclusion, the FAS measurement can facilitate caring interventions.

The Facial Affective Scale (FAS) as a predictor for pain unpleasantness when children undergo immunizations

Needle fear is a common problem in children undergoing immunization. To ensure that the individual child’s needs are met during a painful procedure it would be beneficial to be able to predict whether there is a need for extra support. The self-reporting instrument facial affective scale (FAS) could have potential for this purpose. The aim of this study was to evaluate whether the FAS can predict pain unpleasantness in girls undergoing immunization. Girls, aged 11-12 years, reported their expected pain unpleasantness on the FAS at least two weeks before and then experienced pain unpleasantness immediately before each vaccination. The experienced pain unpleasantness during the vaccination was also reported immediately after each immunization. The level of anxiety was similarly assessed during each vaccination and supplemented with stress measures in relation to the procedure in order to assess and evaluate concurrent validity. The results show that the FAS is valid to predict pain unpleasantness in 11-12-year-old girls who undergo immunizations and that it has the potential to be a feasible instrument to identify children who are in need of extra support to cope with immunization. In conclusion, the FAS measurement can facilitate caring interventions.

Learning from facial aging patterns for automatic age estimation

Age Specific Human-Computer Interaction (ASHCI) has vast potential applications in daily life. However, automatic age estimation technique is still underdeveloped. One of the main reasons is that the aging effects on human faces present several unique characteristics which make age estimation a challenging task that requires non-standard classification approaches. According to the speciality of the facial aging effects, this paper proposes the AGES (AGing pattErn Sub-space) method for automatic age estimation. The basic idea is to model the aging pattern, which is defined as a sequence of personal aging face images, by learning a representative subspace. The proper aging pattern for an unseen face image is then determined by the projection in the subspace that can best reconstruct the face image, while the position of the face image in that aging pattern will indicate its age. The AGES method has shown encouraging performance in the comparative experiments either as an age estimator or as an age range estimator.

Automatic age estimation based on facial aging patterns

While recognition of most facial variations, such as identity, expression, and gender, has been extensively studied, automatic age estimation has rarely been explored. In contrast to other facial variations, aging variation presents several unique characteristics which make age estimation a challenging task. This paper proposes an automatic age estimation method named AGES (AGing pattErn Subspace). The basic idea is to model the aging pattern, which is defined as the sequence of a particular individual's face images sorted in time order, by constructing a representative subspace. The proper aging pattern for a previously unseen face image is determined by the projection in the subspace that can reconstruct the face image with minimum reconstruction error, while the position of the face image in that aging pattern will then indicate its age. In the experiments, AGES and its variants are compared with the limited existing age estimation methods (WAS and AAS) and some well-established classification methods (kNN, BP, C4.5, and SVM). Moreover, a comparison with human perception ability on age is conducted. It is interesting to note that the performance of AGES is not only significantly better than that of all the other algorithms, but also comparable to that of the human observers.

syn-Facial hetero-bridged [n]polynorbornanes : a new class of polarofacial framework molecules composed of fused 7-oxa- and 7-azanorbornanes

New oxygen-bridged norbornane-fused cyclobutene epoxides and bis-(cyclobutene epoxides) are described and shown to react stereoselectively with 7-azanorbornenes to produce syn-facial N,O-bridged polynorbornanes and stereorandomly with 7-oxanorbornenes to produce O,O-bridged polynorbornanes as mixtures of syn-facial and anti-facial products.[1] Polarofacial systems containing up to six syn-facial norbornane bridges are described, while systems with seven co-facial oxygen atoms have been prepared by incorporating terminal epoxide rings to O5-[5]polynorbornanes. Ester-substituted 1,3,4-oxadiazoles are shown to be useful reagents for coupling 7-oxanorbornanes and produce predominantly syn-facial O-bridged polarofacial systems together with their anti-facial isomers.

The Influence of Gender and Facial Appearance on Voting Practices

Women’s faces tend to naturally retain more neonate features than men. These features, such as a greater eye height, a smaller nose area, and a wider smile, would cause women to have more immature faces than males. Interestingly, women who have these facial features are often perceived as more attractive than women with mature facial features. These findings imply that women would be judged less competent than men, and that immature-faced women would be perceived as less competent and more attractive than mature-faced females. Given the direction of political leadership in our country, this has interesting implications for females that are vying for leadership positions. Thus, our study examined the effects of both candidate gender and facial features on voting likelihood, and perceptions of attractiveness and competence, by pairing pictures with neutral party platforms.

Constructing artificial images of facial expressions

This paper presents a method for construction of artificial images of facial expressions. The proposed fractal-based synthesis procedure called pixel-based correspondence works on 2D images and does not require any depth information. This method can generate artificial images of an object when only a single image is given. Using the proposed method, effective example-based facial analysis systems can be trained and utilised in various applications.

Facial age estimation by nonlinear aging pattern subspace

Human age estimation by face images is an interesting yet challenging research topic emerging in recent years. This paper extends our previous work on facial age estimation (a linear method named AGES). In order to match the nonlinear nature of the human aging progress, a new algorithm named KAGES is proposed based on a nonlinear subspace trained on the aging patterns, which are defined as sequences of individual face images sorted in time order. Both the training and test (age estimation) processes of KAGES rely on a probabilistic model of KPCA. In the experimental results, the performance of KAGES is not only better than all the compared algorithms, but also better than the human observers in age estimation. The results are sensitive to parameter choice however, and future research challenges are identified.

Pixel-based correspondence for facial images

A pixel-based correspondence method is presented for representation of facial images. The proposed method consists of two modules: face-image matching and face-image morphing. In the face-image matching module, the correspondence between two images are calculated for all pixel locations. A novel area-based matching method is proposed that makes use of the concept of the fractal dimension, and develops a non-parametric local transform as a basis for establishing correspondence between two face images. In the face-image morphing module, a mapping is performed for deformation of the source face image onto the target face image. This is done to map the pixels in the source face image to the location of their corresponding pixels in the target image.

Facial image analysis

This thesis focuses on novel technologies for facial image analysis, which involves three topics: face recognition under uncontrolled conditions, automatic facial age estimation, and context-aware fusion of face and gait. They are either key issues bridging laboratorial research and real applications, or innovative problems that have barely been studied before.

Facial affect recognition and schizotypal personality characteristics

Aim: Deficits in facial affect recognition are well established in schizophrenia, yet relatively little research has examined facial affect recognition in hypothetically psychosis-prone or ‘schizotypal’ individuals. Those studies that have examined social cognition in psychosis-prone individuals have paid little attention to the association between facial emotion recognition and particular schizotypal personality features. The present study therefore sought to investigate relationships between facial emotion recognition and the different aspects of schizotypy.

Methods: Facial affect recognition accuracy was examined in 50 psychiatrically healthy individuals assessed for level of schizotypy using the Schizotypal Personality Questionnaire. This instrument provides a multidimensional measure of schizophrenia proneness, encompassing ‘cognitive-perceptual’, ‘interpersonal’ and ‘disorganized’ features of schizotypy. It was hypothesized that the cognitive-perceptual and interpersonal aspects of schizotypy would be associated with difficulties identifying facial expressions of emotion during a forced-choice recognition task using a standardized series of colour photographs.

Results: As predicted, interpersonal aspects of schizotypy (particularly social anxiety) were associated with reduced accuracy on the facial affect recognition task, but there was no association between affect recognition accuracy and cognitive-perceptual features of schizotypy.

Conclusions: These results suggest that subtle deficits in facial affect recognition in otherwise psychiatrically healthy individuals may be related to the vulnerability for interpersonal communication difficulties, as seen in schizophrenia.

Low cost multimodal facial recognition via kinect sensors

This paper presents a novel low cost 3D facial recognition using gaming sensors such as Kinect™. The paper describes the hardware, calibration and infrared noise and pattern interference challenges of integrating multiple Kinect sensors. The preliminary results show a promising trend for low cost solutions that can be populated in crowded facilities such as malls and airports.